Imagry (israel) Ltd

Majed Jubeh

Adham Ghazali

Sergey Ten

Ariel Keselman

Suhail Habib

Itai Orr

Jose Ariel Keselman

Abed Abu Dbai

Producing a motion planning policy for an Autonomous Driving Machine (ADM) may include producing a search tree, including a root node representing a current condition of the ADM and derivative nodes linked thereto, representing predicted conditions of the ADM, following application of an action on the ADM. The nodes may be interlinked by actions and associated quality factors. A neural network (NN) may select a plurality of quality factors. The search tree may be expended to add interlinked derivative nodes according to the NN's selection, until a terminating condition is met. Backward propagating and updating one or more quality factors along trajectories of the expanded tree may occur. The NN may be trained according to the current condition of the ADM and the updated quality factors to select an optimal action. The selected optimal action may be applied on at least one physical element of the ADM.

System and method for motion planning of an autonomous driving machine

A system and method of image classification may include linking between a plurality of predetermined objects and categories, assigning a set of attributes for each object until relationships between all categories are determined, storing at least one classified image for each object in an object database, receiving an unclassified image, comparing the received unclassified image to the at least one classified image, determining at least one attribute of the received unclassified image, based on the comparison and based on the assigned attributes for each object, assigning a classification of the received unclassified image, with correlation between the at least one determined attribute of the received unclassified image, and relationships between the categories.

System and method for image classification

A method and a system for training a machine-learning model to identify real-world elements using a simulated environment (SE) may include (a) receiving at least one set of appearance parameters, corresponding to appearance of real-world element; (b) generating one or more realistic elements, each corresponding to a variant of at least one real-world element; (c) generating one or more abstract-elements; (d) placing the elements within the SE; (e) producing at least one synthetic image from the SE; (f) providing the at least one synthetic image to a machine-learning model; and (g) training the machine-learning model to identify at least one real-world element from the at least one synthetic image, that corresponds to at least one realistic element in the SE.

System and method for training a machine-learning model to identify real-world elements

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Majed Jubeh